class_docstr: Support-vector machine (SVM) classification model.
      
docstr: Gets the classification results for the input sample.
  
  :param X: the input sample. Internally, it will be converted to
      ``dtype=np.float32``, and if a sparse matrix is provided -
      to a sparse ``csr_matrix``.
  :type X:  {array-like, sparse matrix} of shape (n_samples, n_features)
  
  :return: predictions of the input samples.
  :rtype: *generator of ndarray of shape (n_samples, n_classes)*
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code: def classify(self, X):
      """Gets the classification results for the input sample.
  
      :param X: the input sample. Internally, it will be converted to
          ``dtype=np.float32``, and if a sparse matrix is provided -
          to a sparse ``csr_matrix``.
      :type X:  {array-like, sparse matrix} of shape (n_samples, n_features)
  
      :return: predictions of the input samples.
      :rtype: *generator of ndarray of shape (n_samples, n_classes)*
      """
      x = convert_data(X)
      return self.internal.classify(*get_data(x))
  
file_path: neoml/NeoML/Python/neoml/SVM.py
imports: import numpy
  from .Utils import convert_data, get_data
  from scipy.sparse import csr_matrix, issparse
  import neoml.PythonWrapper
label_desc: Use image node_id 2 for calling the SvmClassificationModel obj's underlying member method code with example usage: obj.classify(X) and returns: self